I am trying to build a regression model for price sensitivity. I want to be able to show the number of products ordered based on changes to the list price and out of pocket price for a given product.
After getting a base level assessment, we tested 8 scenarios of price changes, each of which is represented by its own variable. Currently there are 9 independent variables influencing the number of products ordered (my dependent variable). They are as follows:
base List Price = 2,500; Out of Pocket Price(OOP) = 250 (This is the current pricing scenario today and used as the baseline)
scenario_a List Price = 1,250; OOP = 250
scenario_b List Price = 750; OOP = 250
scenario_c List Price = 2,500; OOP = 125
scenario_d List Price = 2,500; OOP = 75
scenario_e List Price = 1,250; OOP = 125
scenario_f List Price = 750; OOP = 75
scenario_g List Price = 1,250; OOP = 375
scenario_h List Price = 3,750; OOP = $125
I originally thought I would do a multiple non-linear regression model however I realized I am oversimplifying the problem in my head.
Firstly, I'm confused as to how to handle the 2 price components of each variable (scenario). I thought about splitting these apart but since the number of products ordered is dependent on both of these values in each of the scenarios I feel like that would really be the wrong way to go.
I also considered grouping the different scenarios with common List Prices (group 1 - a,e,g; group 2 - b & f... etc) and conducting multiple regressions based on those but I'm not sure if that even makes sense.
Long story short, at least to start out, I need to figure out the appropriate type of regression to run and how, if at all, I need to manipulate / handle the pricing scenario variables to account for the 2 different pricing components.
Also if my syntax is messed up I will do what I need to to fix it. Hopefully it doesn't need to be changed at all but looking at the preview, I'm anticipating the list will get messed up.